1. Field of the Invention
The present invention relates to an image processing apparatus using a neural network and, more particularly, to an image processing apparatus which performs image binary-to-multi-value conversion for restoring original multi-value image data from binary image data obtained by eliminating information of the multi-value image data, area discrimination processing for discriminating whether an area of a certain image is a halftone area or a character area, and the like.
2. Related Background Art
As a conventional method of estimating an original multi-value image on the basis of a binary image obtained by binarizing the multi-value image, a method using a smoothing filter shown in FIGS. 10A and 10B, or a method using a hierarchical neural network is used.
However, the smoothing filter cannot simultaneously and satisfactorily convert a halftone area and a character area into multi-value data. For example, when a small window shown in FIG. 10A is used, a character area can be satisfactorily converted into multi-value data, but granularity remains in a halftone area. On the other hand, when a large window shown in FIG. 10B is used, a halftone area can be satisfactorily converted into multi-value data, but a character area is blurred.
In the method using a neural network, as the number of pieces of input information increases, multi-value conversion can be attained more satisfactorily, and the above-mentioned drawbacks of the smoothing filter can be compensated for. However, in a conventional neural network, neurons of an input layer are connected to those of an intermediate layer, and neurons of the intermediate layer are connected to those of an output layer. Hence, owing to processing in each neuron based on this coupled state, a huge calculation amount is required, and if such processing is realized in a software manner, a considerably long period of time is required. For example, multi-value conversion of an image having 256.times.256 picture elements (or pixels) and 3 colors requires about 12 minutes when it is realized by a neural network having 49 neurons in an input layer, 25 neurons in an intermediate layer, and 11 neurons in an output layer. For this reason, demand has arisen for a hardware arrangement of a neural network. However, currently announced neuro chips are very expensive and are not practical.